Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use ankitkupadhyay/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ankitkupadhyay/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ankitkupadhyay/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ankitkupadhyay/outputs") model = AutoModelForSequenceClassification.from_pretrained("ankitkupadhyay/outputs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0502b16a06709522eb82539f0764cd53e3b0b851e4d4eb7cbbeb28e73749b50b
- Size of remote file:
- 568 MB
- SHA256:
- d39d7dd9d8a4ba73518b4bb089d4b9890293ff9d036acd6053fedb1f32d900e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.